OHI British Columbia | OHI Science | Citation policy
This script prepares scores (status and trend) for species richness in British Columbia’s coastal regions. Spatial data from IUCN and Aquamaps is combined with extinction risk information from IUCN and conservation rank info based on province-level NatureServe categories.
Currently, the Species Richness sub-goal model is identical to the OHI Global model: a region’s status is based upon an area-weighted average of species health across each BC reporting region.
From Halpern et al (2012):
The target for the Species sub-goal is to have all species at a risk status of Least Concern. We scaled the lower end of the biodiversity goal to be 0 when 75% species are extinct, a level comparable to the five documented mass extinctions and would constitute a catastrophic loss of biodiversity. The Status of assessed species was calculated as the area- and threat status-weighted average of the number of threatened species within each 0.5 degree grid cell.
Mean risk status per cell:
\[\bar{R}_{cell} = \frac{\displaystyle\sum_{species}(Risk)}{n_{spp}}\]
Mean risk status per region:
\[\bar{R}_{SPP} = \frac{\displaystyle\sum_{cells}(\bar{R}_{cell} * A_{cell} * pA_{cell-rgn})}{A_{rgn}}\]
Species goal model
\[X_{SPP} = \frac{((1 - \bar{R}_{SPP}) - 0.25)}{(1 - 0.25)} * 100%\]
where:
AquaMaps
IUCN Red List spatial data: species range map shapefiles
IUCN Red List species index: list of all IUCN red list species, incl IUCN species ID and extinction risk category
NatureServe/BC CDC conservation rank info from BC Species and Ecosystems Explorer:
NOTE: Davies and Baum (2012) could potentially provide a small number of additional species, as in the California Current OHI, but for now we are not considering this in our assessment.
Currently this uses the global species lookup table. How this list is generated (file locations are in ~/github/ohiprep/globalprep/SPP_ICO):
ingest_iucn.R:
spp_iucn_mar.csv in git-annex.spp_fxn.R::create_spp_master_lookup():
ingest_iucn.Rfull_join() the two species listsspatial_source field.Variables in spp_all: am_sid | am_cat | sciname | iucn_sid | pop_trend | pop_cat | spp_group | id_no | iucn_subpop | spatial_source | cat_score | trend_score
| sciname | am_sid | iucn_sid | pop_trend | pop_cat | spatial_source |
|---|---|---|---|---|---|
| Apolemichthys trimaculatus | Fis-26169 | 165835 | Stable | LC | iucn |
| Apolemichthys xanthotis | Fis-28014 | 165853 | Stable | LC | iucn |
| Apolemichthys xanthurus | Fis-28015 | 165844 | Unknown | LC | iucn |
| Centropyge acanthops | Fis-27342 | 155083 | Stable | LC | iucn |
| Centropyge argi | Fis-22168 | 165837 | Unknown | LC | iucn |
| Centropyge aurantonotus | Fis-34233 | 165865 | Stable | LC | iucn |
Data downloaded from BC Species and Ecosystems Explorer includes information on global status and provincial status for species, as assessed by NatureServe.
See this table for info on NatureServe codes
spp_all <- spp_append_bcsee(spp_all)
spp_all <- spp_all %>%
mutate(iucn_score = cat_score,
cat_score = ifelse(!is.na(status_pr_score), status_pr_score, cat_score)) %>%
filter(!is.na(cat_score))
### NOTE: if we want to exclude BLI data, this is where we would have to do it
# spp_all1 <- spp_all %>%
# mutate(spatial_source = ifelse(spatial_source == 'iucn-bli' & !is.na(am_sid), 'am', spatial_source)) %>%
# filter(!spatial_source == 'iucn-bli')
write_csv(spp_all, file.path(dir_goal, 'int/spp_all.csv'))
# DT::datatable(spp_all %>%
# dplyr::select(sciname, am_sid, iucn_sid, pop_trend,
# IUCN_cat = pop_cat, NS_cat = status_pr, spatial_source) ,
# rownames = FALSE,
# caption = 'BC-specific species list:',
# class = 'stripe hover compact',
# options = list(dom = 'tp'))Using OHIBC region polygons, determine 0.5° raster cells corresponding to each region.
## Reading OHIBC regions shapefile...
## ~/github/ohibc/prep/spatial/ohibc_rgn_wgs84.shp
Note that subs() can’t deal with duplicate cell IDs; for cells included in two or more regions, displaying the minimum region ID value
These species-per-cell tables are generated from global SPP goal data, from AquaMaps data and IUCN range maps, filtered to just OHIBC regions.
In this section we perform the model calculations:
Category value range: least concern = 0, extinct = 1
Status and trend layers saved to data folder.
## Writing SPP status and trend based only on IUCN categories to:
## ~/github/ohibc/prep/spp_ico/v2016/output/spp_status.csv
## ~/github/ohibc/prep/spp_ico/v2016/output/spp_trend.csv
The maps and tables show status based upon rankings from NatureServe (where available) and IUCN (where NatureServe is not available). Currently no trend information is taken from NatureServe, so trend is based entirely on IUCN rankings.
Create final outputs for 3nm zone for resilience calculations. In this step, rather than using full assessment regions, only the three-nautical-mile offshore zone is examined.
## Reading OHIBC 3nm offshore regions shapefile...
## ~/github/ohibc/prep/spatial/ohibc_offshore_3nm_wgs84.shp
## Writing SPP status and trend for 3nm regions to:
## ~/github/ohibc/prep/spp_ico/v2016/output/spp_status_3nm.csv
## ~/github/ohibc/prep/spp_ico/v2016/output/spp_trend_3nm.csv